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1 Focused Crawling with ApacheCon North America Vancouver, 2016

2 Hello! I am Sujen Shah Computer University of Southern California Research NASA Jet Propulsion Laboratory Member of The ASF and Nutch PMC since 2015 sujen@apache.org /in/sujenshah

3 Outline The Apache Nutch Project Architectural Overview Focused Crawling Domain Discovery Evaluation Future Additions Acknowledgements

4 Apache Nutch Highly extensible and scalable open source web crawler software project. Hadoop based ecosystem, provides scalability. Highly modular architecture, to allow development of custom plugins. Supports full-text indexing and searching. Multi-threaded robust distributed crawling with configurable politeness. Project website :

5 Nutch History 2003 Started by Doug Cutting and Mike Caffarella MapReduce implementation and Hadoop spin off from Nutch Friends of Nutch 2007 Use MimeType Detection from Tika 2010 Top Level Project at Apache Nutch 2.x released offering storage abstraction via Apache Gora REST API, Publisher/Subscriber, JavaScript interaction and content-based Focused Crawling capabilities

6 Architecture [Diagram courtesy Florian Hartl :

7 Architecture Stores info for URLs: URL Fetch Status Signature Protocols [Diagram courtesy Florian Hartl :

8 Architecture Stores incoming links to each URL and its associated anchor text. [Diagram courtesy Florian Hartl :

9 Architecture Stores: Raw page content Parsed content, outlinks and metadata Fetch-list [Diagram courtesy Florian Hartl :

10 Architecture [Diagram courtesy Florian Hartl :

11 Nutch Workflow Typical workflow is a sequence of batch operations Inject : Populate crawldb from seed list Generate : Selects URLs to fetch Fetch : Fetched URLs from fetchlist Parse : Parse content from fetched URLs UpdateDB : Update the crawldb InvertLinks : Builds the linkdb Index : Optional step to index in SOLR, Elasticsearch, etc

12 Architecture Few more tools at a glance Fetcher : Multi-threaded, high throughput Limit load on servers Partitioning by host, IP or domain Plugins : On demand activation Customizable by the developer Example: URL filters, protocols, parsers, indexers, scoring etc WebGraph : Stores outlinks, inlinks and node scores Iterative link analysis by LinkRank

13 Crawl Frontier The crawl frontier is a system that governs the order in which URLs should be followed by the crawler. Two important considerations [1] : Refresh rate : High quality pages that change frequently should be prioritized Politeness : Avoid repeated fetch requests to a host within a short time span Open Web URL Frontier (refresh rate, politeness, relevance, etc) URLs already fetched [1]

14 Frontier Expansion Manual Expansion: Seeding new URLs from Reference websites (Wikipedia, Alexa, etc) Search engines From prior knowledge Automatic discovery: Following contextually relevant outlinks Cosine similarity, Naive Bayes plugins Controlling by URL filers, regular expressions Using scoring OPIC scoring

15 Broad vs. Focused Crawling Broad Crawling : Unlimited crawl frontier Limited by bandwidth and politeness factors Useful for creating an index of the open web Can achieve high recall Not useful for domain discovery as crawled content may include a lot of irrelevant material Focused Crawling : Limit crawl frontier by calculating relevance of URL Low resource consumption as compared to the above Can achieve high precision Useful for domain discovery as it prioritizes based on content relevance

16 Domain Discovery A Domain, here, is defined as an area of interest for a user. Domain Discovery is the act of exploring a domain of which a user has limited prior knowledge. Domain discovery process may include : Using a focused crawler User providing some prior knowledge in the form of text, questions or reference websites

17 Focused Crawling with Nutch Previously available tools : URL filter plugins Filter based on regular expressions Whitelist/blacklist hosts Filter based on content mimetype Scoring links (OPIC scoring) Breadth first or Depth first crawl Limitations : Follows the link structure Does not capture content relevance to a domain

18 Focused Crawling with Nutch To capture content relevance to a domain, two new tools have been introduced. Cosine Similarity scoring filter Naive Bayes parse filter Nutch JIRA issues :

19 Cosine Similarity Cosine similarity is a measure of similarity between two vectors of an inner product space that measures the cosine of the angle between them [1]. Similarity = cos( ) = A. B / A. B, where A and B are the vectors. Lesser the angle => higher the similarity [1]

20 Cosine Similarity Scoring in Nutch Implemented as a Scoring filter Computed by measuring the angle between two Document Vectors. Document Vector : A term frequency vector containing all the terms occurring on a fetched page. DV = { robots :51, autonomous : 12, artificial : 23,. }

21 Cosine Similarity Scoring - Architecture

22 Cosine Similarity Scoring - Working Features of the similarity scoring plugin : Scores a page based on content relevance Leverages a simplistic bag-of-words approach Outlinks from relevant parent pages are considered relevant Seed

23 Iteration 1 Start with an initial seed Seed is considered to be relevant User provides keyword list for cosine similarity Seed Policy : Fetch top 4 urls in frontier Unfetched (in the crawl frontier) Fetched Decreasing order of relevance All children given same priority as parent in the crawl frontier

24 Iteration 2 Children are fetched by the crawler Similarity against the goldstandard is computed and scores are assigned. Seed Policy : Fetch top 4 urls in frontier Unfetched (in the crawl frontier) Fetched Decreasing order of relevance

25 Iteration 3 Policy : Fetch top 4 urls in frontier Unfetched (in the crawl frontier) Fetched Decreasing order of relevance Seed

26 Iteration 4 Policy : Fetch top 4 urls in frontier Unfetched (in the crawl frontier) Fetched Decreasing order of relevance Seed

27 Iteration 5 Policy : Fetch top 4 urls in frontier Unfetched (in the crawl frontier) Fetched Decreasing order of relevance Seed

28 Naive Bayes Classifier Naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features [1]. Naive Bayes in Nutch Implemented as a parse filter Classifies a fetched page relevant or irrelevant based on a user provided training dataset [1]

29 Naive Bayes Classifier Working User provides a set of labeled examples as training data Create a model based on given training data Classify each page as relevant (positive) or irrelevant(negative)

30 Naive Bayes Classifier Working Features: All outlinks from an irrelevant (negative) page are discarded All outlinks from a relevant (positive) page are followed Seed Crawl Scenario

31 Evaluation The following process was followed to perform domain discovery using the tools discussed earlier: Deploy 3 different Nutch configurations a. Custom Regex-filters and default scoring b. Cosine similarity scoring activated with keyword list c. Naive Bayes filter activated with labeled training data Provide the same seeds to all 3 configurations Crawl was run for 7 iterations [Thanks to Xu Wu for the evaluations]

32 Evaluation Iteration Regex-filters and seed list Domain related Total Rate Domain related Cosine similarity scoring filter Total Rate Domain related Naive Bayes parse filter Total Rate % % % % % % % % % % % % % % % % % % % % % Total % % % [Thanks to Xu Wu for the evaluations]

33 Evaluation [Thanks to Xu Wu for the evaluations]

34 Analysis Page Relevance* for the first 3 rounds is almost the same for all the methods Relevancy sharply rises for the Cosine similarity scoring for further rounds Naive Bayes and custom regex-filters perform almost the same * Page Relevance True Relevance of a fetched page was calculated using MeaningCloud s [1] text classification API. [1]

35 Limitations A few things to consider : The performance of these new focused crawling tools depends on how well the user provides the initial domain relevant data. Keyword/Text for Cosine Similarity Labeled text for Naive Bayes Filter Currently, these tools perform well with textual data, there is no provision for multimedia These techniques are good at providing topically relevant content, but may not provide factually relevant content

36 Future Improvements Potential additions to focused crawling in Nutch : Use the html DOM structure of a page to assess relevance to a domain (ex- news, forums, etc) Augment the goldstandard in Cosine similarity with newly found highly relevant text in between iterations Use Tika s NER Parser and GeoParser to extract entities and locations to capture more metadata about a domain Use Part-of-Speech to capture grammar(context) in a domain (ex- a same key term could occur in various domains)

37 Other cool tools... Nutch REST API Publisher/Subscriber model Headless browsing - Selenium and PhantomJS Real-time graph querying of the web graph (upcoming)

38 Acknowledgements Thanks to : Andrzej Białecki, Chris Mattmann, Doug Cutting, Julien Nioche, Mike Caffarella, Lewis John McGibbney Sebastian Nagel for ideas and material from their previous presentations all Nutch contributors for their amazing work! Florian Hartl for the architecture diagram and blogpost Xu Wu for the evaluations SlidesCarnival for the presentation template

39 Acknowledgements A special thanks to : My mentor Dr. Chris Mattmann for his guidance The awesome team at NASA Jet Propulsion Laboratory And the DARPA MEMEX Program

40 Thanks! Any questions? You can find me

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